Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.

This study aims at the implementation of an Artificial Intelligence approach to the use of music for supporting the neurorehabilitation of children with brain injuries or neurological difficulties.The output of this study will be the implementation of an app for mobile devices with games to be played by pediatric patients, allowing time for their cognitive and motor abilities to recover while enjoying pleasant activities. In particular, a Neural Network Classification approach is proposed in order to automatically adapt the game difficulty to the current cognitive capabilities of the child.

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